As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing field of research typically burdened by an imprecise description of the built environment, significant computational cost, and a lack of high-resolution estimates of the impacts of climate change. Here, we present open-source, computationally efficient machine learning methods that can improve the accuracy of urban temperature estimates when compared to historical reanalysis data. These models are applied to residential buildings in Los Angeles, and we compare the energy benefits of heat mitigation strategies to the impacts of climate change. We find that cooling demand is likely to increase substantially through midcentury, but engineered high-albedo surfaces could lessen this increase by more than 50%. The corresponding increase in heating demand complicates this narrative, but total annual energy use from combined heating and cooling with electric heat pumps in the Los Angeles urban climate is shown to benefit from the engineered cooling strategies under both current and future climates.

The idea is to Build a Knowledge Base, that can be used to trace all activities related to the overall life-cycle of buildings. Since various directives of the EU are related to sustainability, resilience and energy efficiency of building stock, it is necessary to provide a marketplace where various actors can share their offers, including their quality certificates and credentials, and where it would be possible to log and trace every information, activity and change, and use the knowledge to improve sustainability. The project will extend a Digital Building LogBook (DBL), used by a municipality for the management and the administration of its huge set of buildings, with several available and novel data, tools and functionalities, by the help of a Decentralized Knowledge Graph (DKG), an open source blockchain-based solution. DKG software will include specific building-related ontologies, so that the whole knowledge base about the life-cycle of the building can be logged and by that continuously updated, providing mechanisms and interfaces for the relevant stakeholders, to publish, trace, share, tokenize, end even trade models in a market economy. Such information integration can support decisions on optimal adaptation and intervention planning strategies for large populations of buildings.

A large and necessary step in achieving the Paris Agreement requires a transition to a highly efficient building stock in terms of real energy performance. This is perhaps nowhere more true than in Europe, where it is often stated that "all buildings built before 1990 are inefficient" and that up to 75% need renovating in order to reach a higher energy efficiency standard. In order to decarbonise EU building stock by 2050, a vision laid out in the Clean Energy for All Europeans communication (2016), the majority of buildings must be highly energy efficient, meaning they should comply with an Energy Performance Certificate (EPC) label A. Unfortunately, this might prove more difficult than expected. New research from the BPIE shows that although building performance is constantly improving in the EU, only after 2010 was the average building was built to an efficient standard (0.49 W/m2 K for the building envelope) in the European Union. That means only 3% of building stock in the EU does actually qualifies for the A-label, so 97% (not 75% as typically stated) should therefore be upgraded.